# The Roles of Supervised Machine Learning in Systems Neuroscience

arXiv: https://arxiv.org/abs/1805.08239 · HTML: https://mlsanity.com/paper/1805.08239/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 May 2018.

## Built on by (most cited first, 7 of 7)

- [In-Network Learning: Distributed Training and Inference in Networks](https://mlsanity.com/paper/2107.03433/) (2021)
- [A Topological Deep Learning Framework for Neural Spike Decoding](https://mlsanity.com/paper/2212.05037/) (2022)
- [On In-network learning. A Comparative Study with Federated and Split Learning](https://mlsanity.com/paper/2104.14929/) (2021)
- [Dynamical prediction of two meteorological factors using the deep neural network and the long short term memory (1)](https://mlsanity.com/paper/2101.09356/) (2021)
- [Dynamical prediction of two meteorological factors using the deep neural network and the long short-term memory (2)](https://mlsanity.com/paper/2104.14406/) (2021)
- [Harnessing Near-Infrared Spectroscopy and Machine Learning for Traceable Classification of Hanwoo and Holstein Beef](https://mlsanity.com/paper/2507.02903/) (2025)
- [Dopamine-driven synaptic credit assignment in neural networks](https://mlsanity.com/paper/2510.22178/) (2025)

## Abstract (arXiv)

Over the last several years, the use of machine learning (ML) in neuroscience has been rapidly increasing. Here, we review ML's contributions, both realized and potential, across several areas of systems neuroscience. We describe four primary roles of ML within neuroscience: 1) creating solutions to engineering problems, 2) identifying predictive variables, 3) setting benchmarks for simple models of the brain, and 4) serving itself as a model for the brain. The breadth and ease of its applicability suggests that machine learning should be in the toolbox of most systems neuroscientists.